I was perusing Jeff Sagarin’s computer ratings this morning. His composite ranking places Cal 17th in the country overall, though his Predictor rating (which is more accurate, though less politically correct because it takes into account margin of victory) places them 28th.

I was more amused by the ratings of the teams Cal has played this year. Talk about three stinkers: Maryland is rated 115th overall (below a few I-AA schools), Washington State is rated 119th, and I-AA Eastern Washington is rated just one slot below the Cougars at 120th.

Cal’s other wins were over Sagarin #42 UCLA and Sagarin #59 Minnesota.

In contrast, Cal’s losses? To Sagarin #3 Oregon and Sagarin #5 USC. That’s the good news. The bad news? Cal has yet to play Sagarin #14 Arizona, Sagarin #23 Stanford, and Sagarin #24 Oregon State. In terms of the pure Predictor rating, Sagarin would predict that Cal will finish the season 9-3, with wins over Arizona, ASU, and Washington and a loss to Stanford (!). I can’t really call that an unreasonable prediction. It might even be optimistic.

But as always, computers don’t play the games. So we’ll see.

For what it’s worth, Cal’s current ratings in the computer systems that make up a portion of the BCS standings: 26, 17, 27, 20, 15, and 21, for an average computer rating of 20th in the country. (Hat tip: Jerry Palm’s CollegeBCS site.)

5 Responses to “
Strength (and Weakness) of Schedule ”

Actually, when you add in home-field advantage, Sagarin predicts a close victory over OSU and basically a pick-’em game vs. Arizona. With wins @ASU and @UW and a loss @Stanford, that gives the Bears either a 9-3 or 8-4 record.

As it is, he’s calling us a 2-point favorite in Tempe this weekend, and that’s the only game worth worrying about right now.

Well, clearly the predictor rating is slammed by the Bears losing so dramatically to Oregon and USC. The Bears were stunned in those games, and it seems like if you rely on those games to develop a mathematical model, your prediction will be overdone. We are not THAT bad to where we would lose those games by those margins if we played those games 10 to 20 times. The scores would be a lot closer, and we would actually win some of the games.

I think if the Bears offense can play decently then there is no game that we can not win. There are no dominant defenses that we are left playing against. I think in this instance, the ELO chess model is a better predictor of Cal than the predictor model. I mean, at team that averages 45 points in 5 games and 3 in two games, one of the two is an outlier. UCLA and Washington State were not supposed to have totally crappy defenses. And even against SC, if riley could have connected on a few open guys, that would have been a totally different game.

@oski88- I dunno. I think it matters that Cal lost to USC by 27 at home and Oregon State lost to them by 6 at USC. I think that actually does say a lot about the difference between OSU and Cal, and it makes me think OSU is probably better than Cal. Of course, margin of victory is always somewhat shaky because teams tend to take their foot off the gas when they’re way ahead — Cal could have easily hung 70 on WSU yesterday, for example. But over the years, Sagarin has observed that his Pure Points method is the best predictor of future outcomes. The BCS just can’t use it because it encourages coaches to run up the score.